# Integrating Clinical and Imaging Markers for Survival Prediction in Advanced NSCLC Treated with EGFR-TKIs

## TL;DR

This study developed a survival prediction model for advanced lung cancer patients with EGFR mutations, using clinical and imaging markers to predict 18-month mortality.

## Contribution

A novel prognostic model combining clinical and radiologic factors for predicting mortality in EGFR-mutated NSCLC patients treated with TKIs.

## Key findings

- Key predictors of mortality include BMI, bone metastasis, neutrophil-to-lymphocyte ratio, albumin-to-globulin ratio, and pulmonary artery diameter.
- The model achieved a Harrell’s C-statistic of 0.72, indicating good predictive accuracy.
- A web-based tool was developed to implement the model for clinical use.

## Abstract

A high prevalence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) has been observed among the Asian population, along with significantly lower survival rates reported in previous real-world studies compared to clinical trials. Therefore, a precise survival prediction model for this patient subgroup is crucial. A prognostic model was developed using fundamental clinical and non-cancerous radiologic parameters to predict 18-month mortality in patients with EGFR-mutated advanced NSCLC. Key factors included a BMI <18.5 or ≥23, presence of bone metastasis, neutrophil-to-lymphocyte ratio ≥ 5, albumin-to-globulin ratio < 1, and mean pulmonary artery diameter ≥ 29 mm. The model demonstrated good predictive accuracy and is available as a web-based tool for clinical use.

Background: Epidermal growth factor receptor (EGFR) mutations are presented in approximately 50% of East Asian populations with advanced non-small cell lung cancer (NSCLC). While EGFR-tyrosine kinase inhibitors (TKIs) are the standard treatment, patient outcomes are also influenced by host-related factors. This study aimed to investigate clinical and radiological factors associated with early mortality and develop a prognostic prediction model in advanced EGFR-mutated NSCLC. Methods: A retrospective cohort was conducted in patients with EGFR-mutated NSCLC treated with first line EGFR-TKIs from January 2012 to October 2022 at Chiang Mai University Hospital. Clinical data and radiologic findings at the initiation of treatment were analyzed. A multivariable flexible parametric survival model was used to determine the predictors of death at 18 months. The predicted survival probabilities at 6, 12, and 18 months were estimated, and the model performance was evaluated. Results: Among 189 patients, 84 (44.4%) died within 18 months. Significant predictors of mortality included body mass index <18.5 or ≥23, bone metastasis, neutrophil-to-lymphocyte ratio ≥ 5, albumin-to-globulin ratio < 1, and mean pulmonary artery diameter ≥ 29 mm. The model demonstrated good performance (Harrell’s C-statistic = 0.72; 95% CI: 0.66–0.78). Based on bootstrap internal validation, the optimism-corrected Harrell’s C-statistic was 0.71 (95% CI: 0.71–0.71), derived from an apparent C-statistic of 0.75 (95% CI: 0.74–0.75) and an estimated optimism of 0.04 (95% CI: 0.03–0.04). Estimated 18-month survival ranged from 87.1% in those without risk factors to 2.1% in those with all predictors. A web-based tool was developed for clinical use. Conclusions: The prognostic model developed from fundamental clinical and radiologic parameters demonstrated promising utility in predicting 18-month mortality in patients with advanced EGFR-mutated NSCLC receiving first-line EGFR-TKI therapy.